Methods and apparatus for constructing low-frequency seismic inversion models

Abstract

This application discloses a method and apparatus for constructing a low-frequency seismic inversion model, belonging to the field of geophysical exploration technology. The method includes: acquiring seismic data volume and well logging data of a target area; determining first geological attribute data of the target area based on the well logging data; determining a stratigraphic framework model of the target area based on the seismic data volume of the target area; determining the target spatial range of a target geological body of a target geological type within the target area based on the seismic data volume of the target area; determining second geological attribute data corresponding to the target spatial range; and determining a low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range. Using this application, the low-frequency model can accurately reflect the specific geological conditions within the actual strata, thereby improving the accuracy of seismic inversion using this low-frequency model.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method and apparatus for constructing a low-frequency seismic inversion model. Background Technology

[0002] Seismic inversion is a core technology in oil and gas exploration and development, and its methods include recursive inversion, model-based inversion, and seismic attribute inversion. Among these, model-based inversion methods have advantages such as high accuracy and high resolution, enabling detailed descriptions of oil and gas reservoirs. The key to model-based inversion methods lies in establishing a low-frequency model (also known as the initial model or background model).

[0003] Currently, the general method for establishing low-frequency models is to first establish a formation framework model, and then use well logging data for mathematical interpolation under the constraints of the formation framework model to obtain the low-frequency model.

[0004] However, the low-frequency model obtained using the above method cannot accurately reflect the special geological conditions in the real strata, such as the presence of carbonate caves, river intrusions, and igneous intrusions. As a result, the seismic inversion using this low-frequency model cannot reflect the real geological conditions, and the accuracy of the inversion results is low. Summary of the Invention

[0005] This application provides a method and apparatus for constructing a low-frequency seismic inversion model, which can solve the problem that seismic inversion using low-frequency models obtained by existing technologies cannot reflect the true geological conditions and has low accuracy of inversion results.

[0006] In a first aspect, a method for constructing a low-frequency seismic inversion model is provided. The method includes: acquiring seismic data volume and well logging data of a target area; determining first geological attribute data of the target area based on the well logging data, wherein the first geological attribute data is one or more of the following: acoustic impedance data, density data, natural gamma data, sonic transit time data, permeability data, porosity data, resistivity data, electrical conductivity data, water saturation data, potential data, and formation temperature data; determining a stratigraphic framework model of the target area based on the seismic data volume of the target area; determining a target spatial range of a target geological body of a target geological type within the target area based on the seismic data volume of the target area; determining second geological attribute data corresponding to the target spatial range; and determining a low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range.

[0007] In one possible implementation, the target geological type is one or more of carbonate caves, river channels, and igneous bodies.

[0008] In one possible implementation, determining the target spatial range of a target geological body of a target geological type within the target area based on the seismic data volume of the target area includes: determining the seismic attribute volume of the target area based on the seismic data volume of the target area; and determining the target spatial range of a target geological body of a target geological type within the target area based on the seismic attribute volume of the target area.

[0009] In one possible implementation, the first geological attribute data of the target area includes geological attribute values ​​corresponding to multiple sampling time points within an exploratory well in the target area, wherein the exploratory well is a well used to detect the logging data; the seismic attribute body includes seismic attribute values ​​corresponding to multiple sampling time points within the target area; and the second geological attribute data corresponding to the target spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the target spatial range. Determining the second geological attribute data corresponding to the target spatial range includes: determining the target geological attribute value of the corresponding target sampling time point within the target spatial range from the first geological attribute data of the target area; and determining the second geological attribute data corresponding to the target spatial range based on the target sampling time point and the corresponding target geological attribute value; or obtaining the target geological attribute value corresponding to the geological type of the target geological body; setting the geological attribute values ​​corresponding to all sampling time points within the target spatial range as the target geological attribute value to obtain the second geological attribute data corresponding to the target spatial range.

[0010] In one possible implementation, determining the low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range includes: interpolating the first geological attribute data based on the stratigraphic framework model to obtain an initial low-frequency model of the target area, wherein the initial low-frequency model of the target area includes low-frequency geological attribute values ​​corresponding to multiple sampling time points within the target area; and replacing the data corresponding to the target spatial range in the initial low-frequency model with the second geological attribute data corresponding to the target spatial range to obtain the low-frequency model of the target area.

[0011] In one possible implementation, the second geological attribute data corresponding to the spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the spatial range; the first geological attribute data includes geological attribute values ​​corresponding to multiple sampling time points within the target area; and the stratigraphic framework model includes vector data representing multiple stratigraphic planes and fault planes within the target area. The step of determining the low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range includes: for each sampling time point among the multiple sampling time points within the target area, if the sampling time point is within the target spatial range... Based on the geological attribute values ​​at the sampling time point in the second geological attribute data corresponding to the target spatial range and the vector data of multiple strata and fault planes representing the target area in the stratigraphic framework model, a low-frequency geological attribute value for the sampling time point is generated. If the sampling time point is not within the target spatial range, a low-frequency geological attribute value for the sampling time point is generated based on the second geological attribute value corresponding to the sampling time point in the first geological attribute data and the vector data of multiple strata and fault planes representing the target area in the stratigraphic framework model. The low-frequency geological attribute value of each sampling time point within the target area is determined as the low-frequency model of the target area.

[0012] In one possible implementation, the seismic data volume includes seismic wave amplitude values ​​corresponding to multiple sampling time points within the target area. The step of determining the target spatial range information of the target geological body within the target area based on the seismic data volume of the target area includes: for each sampling time point in the seismic data volume, if the seismic wave amplitude value corresponding to the sampling time point satisfies a first numerical condition among multiple pre-stored numerical conditions, then recording that the sampling time point corresponds to the first numerical condition; for each numerical condition, determining the spatial range corresponding to the closed spatial region that each sampling time point corresponding to the numerical condition can form, as the target spatial range of the target geological body within the target area.

[0013] Secondly, an apparatus for constructing a low-frequency seismic inversion model is provided. The apparatus includes: an acquisition module for acquiring seismic data volume and well logging data of a target area; a determination module for determining first geological attribute data of the target area based on the well logging data, wherein the first geological attribute data is one or more of the following: acoustic impedance data, density data, natural gamma data, sonic transit time data, permeability data, porosity data, resistivity data, conductivity data, water saturation data, potential data, and formation temperature data; determining a stratigraphic framework model of the target area based on the seismic data volume of the target area; determining a target spatial range of a target geological body of a target geological type within the target area based on the seismic data volume of the target area; determining second geological attribute data corresponding to the target spatial range; and determining a low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range.

[0014] In one possible implementation, the target geological type is one or more of carbonate caves, river channels, and igneous bodies.

[0015] In one possible implementation, the determining module is configured to: determine the seismic attribute volume of the target area based on the seismic data volume of the target area; and determine the target spatial range of the target geological body of the target geological type within the target area based on the seismic attribute volume of the target area.

[0016] In one possible implementation, the first geological attribute data of the target area includes geological attribute values ​​corresponding to multiple sampling time points within an exploratory well in the target area, wherein the exploratory well is a well used to detect the logging data; the seismic attribute body includes seismic attribute values ​​corresponding to multiple sampling time points within the target area; and the second geological attribute data corresponding to the target spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the target spatial range. The determining module is configured to: determine the target geological attribute value of the corresponding target sampling time point within the target spatial range from the first geological attribute data of the target area; and determine the second geological attribute data corresponding to the target spatial range based on the target sampling time point and the corresponding target geological attribute value; or obtain the target geological attribute value corresponding to the geological type of the target geological body, set the geological attribute values ​​corresponding to all sampling time points within the target spatial range as the target geological attribute value, and obtain the second geological attribute data corresponding to the target spatial range.

[0017] In one possible implementation, the determining module is configured to: interpolate the first geological attribute data based on the stratigraphic framework model to obtain an initial low-frequency model of the target area, wherein the initial low-frequency model of the target area includes low-frequency geological attribute values ​​corresponding to multiple sampling time points within the target area; and replace the data in the initial low-frequency model corresponding to the target spatial range with the second geological attribute data corresponding to the target spatial range to obtain the low-frequency model of the target area.

[0018] In one possible implementation, the second geological attribute data corresponding to the spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the spatial range; the first geological attribute data includes geological attribute values ​​corresponding to multiple sampling time points within the target area; and the stratigraphic framework model includes vector data representing multiple strata and fault planes within the target area. The determining module is configured to: for each sampling time point within the target area, if the sampling time point is within the target spatial range, generate a low-frequency geological attribute value for the sampling time point based on the geological attribute value of the sampling time point in the second geological attribute data corresponding to the target spatial range and the vector data representing multiple strata and fault planes within the target area in the stratigraphic framework model; if the sampling time point is not within the target spatial range, generate a low-frequency geological attribute value for the sampling time point based on the second geological attribute value corresponding to the sampling time point in the first geological attribute data and the vector data representing multiple strata and fault planes within the target area in the stratigraphic framework model; and determine the low-frequency geological attribute value of each sampling time point within the target area as the low-frequency model of the target area.

[0019] In one possible implementation, the seismic data volume includes seismic wave amplitude values ​​corresponding to multiple sampling time points within the target area. The determining module is configured to: for each sampling time point in the seismic data volume, if the seismic wave amplitude value corresponding to the sampling time point satisfies a first numerical condition among multiple pre-stored numerical conditions, then record that the sampling time point corresponds to the first numerical condition; for each numerical condition, determine the spatial range corresponding to the closed spatial region that each sampling time point corresponding to the numerical condition can form, as the target spatial range of the target geological body within the target area.

[0020] Thirdly, a computer device is provided, the computer device including a processor and a memory, the memory for storing computer instructions, and the processor for executing the computer instructions stored in the memory to cause the computer device to perform the method of the first aspect and its possible implementations.

[0021] Fourthly, a computer-readable storage medium is provided, which stores computer program code, such that when the computer program code is executed by a computer device, the computer device performs the method of the first aspect and its possible implementations.

[0022] Fifthly, a computer program product is provided, comprising computer program code, wherein when the computer program code is executed by a computer device, the computer device executes the method of the first aspect and its possible implementations.

[0023] In this embodiment, seismic data volume and well logging data of the target area are first acquired. Based on the well logging data, the first geological attribute data of the target area is determined. Based on the seismic data volume, the stratigraphic framework model of the target area and the target spatial range of the target geological body of the target geological type within the target area are determined. Then, the second geological attribute data corresponding to the target spatial range are determined. Finally, based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range, a low-frequency model of the target area is determined. Thus, the low-frequency model obtained by the above method can accurately reflect the special geological conditions within the actual strata. Consequently, the seismic inversion using this low-frequency model can reflect the true geological conditions, improving the accuracy of the inversion results. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0026] Figure 2 This is a flowchart of a method for constructing a low-frequency seismic inversion model provided in an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the target spatial range of a target geological body provided in an embodiment of this application;

[0028] Figure 4 This is a structural diagram of a device for constructing a low-frequency seismic inversion model provided in an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0031] First, several terms used in the embodiments of this application will be introduced:

[0032] Seismic data volume: also known as 3D seismic data, is data obtained by organizing and processing seismic data into seismic traces as units, and includes the seismic wave amplitude values ​​at the sampling time points of each seismic trace.

[0033] Well logging data, also known as well logging curves or well logging information, contains geological attribute values ​​at sampling depth points of each well. Well logging data can include various types of data, such as density data, natural gamma data, sonic transit time data, permeability data, porosity data, resistivity data, electrical conductivity data, water saturation data, potential data, formation temperature data, time-depth data, and so on.

[0034] Stratigraphic framework model: also known as structural model or structural frame model, can reflect the size, shape, distribution characteristics, boundaries and offsets of strata in the target area, including vector data corresponding to strata and fault planes.

[0035] Seismic attribute volume: This is data obtained by organizing and processing seismic attribute data by seismic trace. Seismic attribute data is data about the geometric, kinematic, dynamic, and statistical characteristics of seismic waves, derived from seismic data through mathematical transformations. It can be divided into time data, amplitude data, frequency data, phase data, waveform data, energy data, attenuation data, etc. The seismic attribute volume contains the seismic attribute values ​​at the sampling time points of each seismic trace.

[0036] Low-frequency model: also known as initial model or background model, is a three-dimensional model that reflects the geological attribute data of low-frequency components, including the low-frequency geological attribute values ​​at each sampling time point.

[0037] This application provides a method for constructing a low-frequency model for seismic inversion, applicable to seismic inversion. After obtaining initial seismic data through seismic exploration, a series of processing steps can be performed on the initial seismic data, such as static correction, pre-stack noise suppression, amplitude compensation, common midpoint stacking, and dynamic correction, to reduce noise interference and improve data quality, resulting in processed seismic data. Then, the processed seismic data can be organized and processed by seismic trace to obtain a seismic data volume (also known as 3D seismic data). Similarly, after obtaining initial well logging data, a series of processing steps such as depth alignment, curve smoothing, environmental correction, numerical standardization, and well-seismic calibration can be performed to obtain processed well logging data (the well logging data referred to in this embodiment is the processed well logging data). Then, a low-frequency model can be constructed based on the seismic data volume and well logging data for seismic inversion. The low-frequency model used in seismic inversion can include various types, such as wave impedance models, density models, porosity models, etc. This application uses the construction of a wave impedance model as an example for illustration; other cases are similar and will not be described in detail here.

[0038] Based on the above application scenarios, this application provides a method for constructing a low-frequency seismic inversion model, which can be implemented by a computer device. This computer device can be a server or a terminal, etc. The terminal can be a desktop computer, laptop computer, tablet computer, mobile phone, etc. The server can be a single server or a server group composed of multiple servers.

[0039] Figure 1 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. From the perspective of hardware composition, the structure of the computer device 10 can be as follows: Figure 1 As shown, it includes a processor 101, a memory 102, and a display unit 103.

[0040] The processor 101 can be a central processing unit (CPU) or a system-on-chip (SoC), etc. The processor 101 can be used to determine the first geological attribute data of the target area based on well logging data, to determine the stratigraphic framework model of the target area based on the seismic data volume of the target area, to determine the spatial range of the target geological body of the target geological type in the target area based on the seismic data volume of the target area, to determine the second geological attribute data corresponding to the spatial range, and to determine the low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model and the second geological attribute data corresponding to the spatial range, etc.

[0041] The memory 102 may include various volatile or non-volatile memories, such as solid-state disks (SSDs) and dynamic random access memory (DRAM). The memory 102 can be used to store pre-stored data, intermediate data, and result data during the process of constructing a low-frequency seismic inversion model. Examples include seismic data volumes, well logging data, first geological attribute data, second geological attribute data, the spatial extent of the target geological body of the target geological type, stratigraphic framework models, low-frequency models, and so on.

[0042] In addition to processor 101 and memory 102, computer device 10 may also include communication component 103 and display component 104.

[0043] The communication component 103 can be a wired network connector, a wireless fidelity (WiFi) module, a Bluetooth module, a cellular network communication module, etc. The communication component 103 can be used to transmit data with other devices, such as servers or terminals. For example, the computer device 10 can receive seismic data, well logging data, etc., and can also send low-frequency models, etc., to a server for storage.

[0044] The display component 104 can be a standalone screen, a screen integrated with the terminal body, a projector, etc. The screen can be a touch screen or a non-touch screen. The display component 104 is used to display system interfaces, application interfaces, etc. For example, the display component 104 can display stratigraphic framework models, the spatial range of target geological bodies of target geological types, and low-frequency models, etc.

[0045] Figure 2 This is a flowchart of a method for constructing a low-frequency seismic inversion model provided in an embodiment of this application.

[0046] See Figure 2 The method may include the following steps:

[0047] 201. Obtain seismic data volume and well logging data for the target area.

[0048] The target area is the region where seismic inversion needs to be performed.

[0049] 202, Determine the first geological attribute data of the target area based on well logging data.

[0050] The first geological attribute data includes one or more of the following: wave impedance data, density data, natural gamma data, sonic transit time data, permeability data, porosity data, resistivity data, electrical conductivity data, water saturation data, potential data, and formation temperature data. The first geological attribute data of the target area includes the geological attribute values ​​corresponding to multiple sampling time points in the exploration wells within the target area. The exploration wells are wells used to detect logging data.

[0051] Well logging data can be in formats such as LAS. It can include density data, natural gamma ray data, sonic transit time data, permeability data, porosity data, resistivity data, conductivity data, water saturation data, potential data, formation temperature data, and time-depth data from the well. The file header can record well identifiers, sampling intervals, and data types. By using pre-stored coordinates corresponding to the well identifiers and sampling intervals, the position of any sampling depth point in the spatial coordinate system of the target area can be determined. Alternatively, the position of any sampling depth point in the spatial coordinate system of the target area can be determined based on pre-stored well identifiers and the depths of sampling depth points stored in the file. Taking density data and time-depth data as examples (see Tables 1 and 2), the file can store the depth of each sampling depth point and the corresponding data value.

[0052] Sampling depth point depth (m) <![CDATA[Density value (g / cm 3 )]]> 0.12500 2.27 0.25000 2.29 0.37500 2.33 …… ……

[0053] Table 1

[0054] Sampling depth point depth (m) Sampling time point (s) 0.10000 1.100 0.20000 2.050 0.30000 3.233 …… ……

[0055] Table 2

[0056] It should be noted that other data types in well logging data can be converted based on the time-depth data, thus transforming depth-domain well logging data into time-domain well logging data. Time-domain well logging data can represent the position of any sampling time point in the time-domain coordinate system of the target area.

[0057] When the primary geological attribute data is acoustic impedance data, it can be calculated from the time-domain acoustic transit time data and the time-domain density data to obtain the time-domain acoustic impedance data. When the primary geological attribute data includes density data, natural gamma ray data, acoustic transit time data, permeability data, porosity data, resistivity data, conductivity data, water saturation data, potential data, and formation temperature data, the depth-domain data can be converted to the time-domain data based on the time-depth data in the well logging data, and then the time-domain data can be used as the primary geological attribute data.

[0058] 203. Determine the stratigraphic framework model of the target area based on the seismic data volume of the target area.

[0059] The stratigraphic framework model includes vector data representing multiple stratigraphic and fault planes within the target area. The seismic data volume can be in formats such as SEG-Y or SEG-2. Referring to Table 3, the seismic data volume can contain seismic wave amplitude values ​​at each seismic trace's sampling time point. Using pre-stored trace number coordinates and sampling intervals, the position of any sampling time point in the time-domain coordinate system of the target area can be determined.

[0060]

[0061] Table 3

[0062] In practice, seismic data volumes of the target area can be input into a machine learning model to obtain bedding plane and fault models for the target area; alternatively, geologists can identify and interpret bedding planes and faults based on the seismic data volumes, and then generate bedding plane and fault models for the target area based on the identification and interpretation results. The bedding plane model can contain vector data representing the stratigraphic bedding planes (spatial geometric surfaces with zero thickness) within the target area, reflecting the size, shape, and distribution characteristics of the stratigraphic bedding planes. Distribution characteristics can be one or more of the following: parallel to the top, parallel to the bottom, isostatic, and seismic-driven. The fault model can contain vector data representing fault bedding planes (spatial geometric surfaces with zero thickness) within the target area, reflecting the boundaries and offsets of the strata within the target area.

[0063] After obtaining the bedding plane model and fault model of the target area, a stratigraphic framework model can be generated based on the bedding plane model and fault model. The stratigraphic framework model can contain vector data representing multiple stratigraphic planes and fault planes within the target area, and can be used to reflect the size, shape, distribution characteristics, boundaries, and offsets of various strata within the target area.

[0064] 204. Based on the seismic data volume of the target area, determine the target spatial range of the target geological body of the target geological type within the target area.

[0065] The target geological types can be varied, such as clastic rocks, shale, carbonate rocks, igneous rocks, and river channels.

[0066] Optionally, the target geological type is one or more of carbonate caves, river channels, and igneous rock formations.

[0067] In implementation, for each sampling time point in the seismic data volume, if the seismic data value corresponding to the sampling time point meets a preset numerical condition among multiple pre-stored numerical conditions, then the sampling time point is recorded as corresponding to the preset numerical condition. There can be various preset numerical conditions; for example, the numerical condition can be a numerical range greater than 8000 and less than 10000, etc. Multiple numerical conditions can be preset. For each numerical condition, the spatial range corresponding to the closed spatial region formed by the sampling time points corresponding to the numerical condition is determined, serving as the target spatial range of the target geological body within the target area.

[0068] Each sampling time point in the seismic data volume is the sampling time point of the corresponding seismic trace in the time domain. Adjacent sampling time points can be considered as consecutive sampling time points. Consecutive sampling time points can form spatial regions such as lines, surfaces, and volumes in the three-dimensional coordinate system of the time domain. Multiple numerical conditions can be pre-stored, such as a first numerical interval, a second numerical interval, etc. Taking the first numerical interval as an example, the range of the first numerical interval is (8000, 10000). If the seismic data value corresponding to the sampling time point is within the first numerical interval, that is, greater than 8000 and less than 10000, then the sampling time point can be recorded as corresponding to the first numerical interval. If among the sampling time points corresponding to the first numerical interval, there are sampling time points that can form a closed spatial region (i.e., multiple consecutive sampling time points form a closed spatial surface or spatial volume), then the spatial range corresponding to the closed spatial region can be used as the target spatial range of the target geological body in the target region. In the seismic data volume, the target spatial range of the target geological body can be referenced. Figure 3 As shown, Figure 3 It is a tangent plane of the seismic data volume along the time axis in the time domain coordinate system. It should be noted that when the sampling time points form a closed spatial surface, there may be sampling time points inside the spatial surface that are not within the first numerical interval. When determining the spatial range corresponding to the closed surface, the range of sampling time points inside the closed surface that are not within the first numerical interval can also be regarded as the target spatial range.

[0069] Optionally, the seismic attribute volume of the target area can be determined based on the seismic data volume of the target area, and the target spatial range of the target geological body within the target area can be determined based on the seismic attribute volume of the target area.

[0070] Seismic attribute data is data obtained by organizing and processing seismic attribute data into seismic traces as units. Seismic attribute data is data on the geometric, kinematic, dynamic, and statistical characteristics of seismic waves, derived from seismic data through mathematical transformations. It can be divided into time data, amplitude data, frequency data, phase data, waveform data, energy data, attenuation data, and so on.

[0071] In practice, calculations can be performed based on the data contained in the seismic data volume to obtain the corresponding seismic attribute data, and then the seismic attribute volume corresponding to that attribute data can be obtained. For example, the frequency data of the seismic wave can be calculated based on the sampling time interval and seismic wave amplitude values ​​contained in the seismic data volume, and then the seismic attribute volume corresponding to the frequency data (this seismic attribute volume can be called the seismic frequency volume) can be obtained. The data format of the seismic attribute volume can be the same as that of the seismic data volume (see Table 3). By using the pre-stored coordinates of the trace number and the sampling interval, the position of any sampling time point in the time domain coordinate system of the target area can be determined.

[0072] For each sampling time point in the seismic attribute body, if the seismic attribute value corresponding to the sampling time point satisfies the first numerical condition among multiple pre-stored numerical conditions, then the sampling time point is recorded as corresponding to the first numerical condition. For each numerical condition, the spatial range corresponding to the closed spatial region that can be formed by each sampling time point corresponding to the numerical condition is determined as the spatial range of the target geological body within the target area.

[0073] It should be noted that when sampling time points form a closed spatial surface, there may be sampling time points within the spatial surface that are not within the first numerical interval. When determining the spatial range corresponding to the closed surface, the spatial range of sampling time points within the closed surface that are not within the first numerical interval can also be used as the target spatial range of the target geological body within the target area. This is because, generally, target geological bodies exist within strata as closed spatial bodies, not as closed spatial surfaces. The reason why sampling time points that meet the numerical conditions form a closed surface is generally because the data of sampling time points within the target geological body do not meet the preset numerical conditions. However, such a closed spatial surface does not conform to the actual geological conditions. Therefore, the spatial range of sampling time points within the closed spatial surface that are not within the preset numerical interval can also be used as the spatial range of the target geological body within the target area, so that the target spatial range of the target geological body conforms to the actual geological conditions.

[0074] Optionally, after determining the spatial range of the target geological body, the sampling time points within the spatial range of the target geological body can be marked. In this way, the sampling time points within the spatial range of the target geological body can be directly retrieved during subsequent processing, thereby improving operational efficiency.

[0075] The process of determining the target spatial extent of a target geological body based on seismic attribute volumes and seismic data volumes is similar to that described above; the relevant processing can be found in the above description.

[0076] Since seismic attribute volumes have higher signal-to-noise ratios and resolutions compared to seismic data volumes, seismic attribute data can be used to more accurately determine the target spatial extent of a target geological body.

[0077] 205. Determine the second geological attribute data corresponding to the target spatial range.

[0078] The second geological attribute data corresponding to the target spatial range includes one or more of the following: wave impedance data, density data, natural gamma data, acoustic transit time data, permeability data, porosity data, resistivity data, electrical conductivity data, water saturation data, potential data, and formation temperature data. The second geological attribute data corresponding to the target spatial range has the same data type as the first geological attribute data. The second geological attribute data corresponding to the target spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the target spatial range.

[0079] In practice, there are several ways to determine the second geological attribute data corresponding to the target spatial range.

[0080] Method 1: In the first geological attribute data of the target area, determine the target geological attribute value of the corresponding target sampling time point within the target spatial range, and based on the target sampling time point and the corresponding target geological attribute value, determine the second geological attribute data corresponding to the target spatial range.

[0081] In the first geological attribute data, the target geological attribute value corresponding to the target sampling time point within the target spatial range (i.e., the spatial range of the geological body of the target type) can be determined. Then, based on the target sampling time point and the corresponding target geological attribute value, the second geological attribute data corresponding to the target spatial range can be determined.

[0082] To determine the target geological attribute value corresponding to the target sampling time point, interpolation can be performed using the target sampling time point and the target geological attribute value under the constraints of the stratigraphic framework model to obtain an intermediate low-frequency model. The geological attribute values ​​of sampling time points in the intermediate low-frequency model with spatial ranges identical to the target spatial range are then used as the second geological attribute data corresponding to the target spatial range. Various interpolation methods can be used, such as distance-weighted methods, kriging, geostatistical methods, and radial basis methods. Alternatively, multiple target geological attribute values ​​can be weighted and summed, and the resulting weighted sum can be set as the second geological attribute data corresponding to the target spatial range.

[0083] Method 2: Obtain the target geological attribute value corresponding to the geological type of the target geological body, set the geological attribute value corresponding to all sampling time points within the target spatial range as the target geological attribute value, and obtain the second geological attribute data corresponding to the target spatial range.

[0084] The target geological attribute value corresponding to the geological type of the target geological body can be an empirical geological attribute value of the target geological type, or an environmental geological attribute value of the target geological type calculated based on the actual exploration environment. The setting method can be to set the pre-stored target geological attribute value as the second geological attribute value corresponding to all sampling time points within the target spatial range, or to set the target geological attribute value entered by the staff as the second geological attribute value corresponding to all sampling time points within the target spatial range, and so on.

[0085] 206. Based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range, a low-frequency model of the target area is determined.

[0086] There are several ways to determine the low-frequency model of the target region.

[0087] Method 1: Based on the stratigraphic framework model, interpolate the first geological attribute data to obtain the initial low-frequency model of the target area. Replace the data corresponding to the spatial range in the initial low-frequency model with the second geological attribute data corresponding to the spatial range to obtain the low-frequency model of the target area.

[0088] The initial low-frequency model of the target area includes geological attribute values ​​corresponding to multiple sampling time points within the target area.

[0089] In implementation, a stratigraphic framework model can be used as a constraint to interpolate the first geological attribute data, obtaining an initial low-frequency model for the target area. Various interpolation methods can be used, such as distance-weighted methods, kriging, geostatistical methods, and radial basis methods. Then, the data from sampling time points in the initial low-frequency model that have the same spatial range as the target spatial range are replaced with the second geological attribute data corresponding to the target spatial range, resulting in the final low-frequency model for the target area.

[0090] Method 2: For each sampling time point among multiple sampling time points within the target area, if the sampling time point is within the target spatial range, then based on the geological attribute value of that sampling time point in the second geological attribute data corresponding to the target spatial range and the vector data of multiple strata and fault planes representing the target area in the stratigraphic framework model, a low-frequency geological attribute value for that sampling time point is generated; if the sampling time point is not within the target spatial range, then based on the geological attribute value corresponding to the sampling time point in the first geological attribute data and the vector data of multiple strata and fault planes representing the target area in the stratigraphic framework model, a low-frequency geological attribute value for that sampling time point is generated, and the low-frequency geological attribute value of each sampling time point within the target area is determined as the low-frequency model of the target area.

[0091] In implementation, if the sampling time point is outside the target space, the first geological attribute data is interpolated under the constraints of the stratigraphic framework model to generate low-frequency geological attribute values ​​and stratigraphic identifiers for that sampling time point. Various interpolation methods can be used, such as distance-weighted methods, kriging, geostatistical methods, and radial basis methods. If the sampling time point is within the target space, the second geological attribute value of that sampling time point is set as the low-frequency geological attribute value for that point, and the stratigraphic identifier corresponding to that sampling time point is generated based on the vector data of stratigraphic and fault planes. Then, the low-frequency geological attribute values ​​of each sampling time point within the target area can be used to determine the low-frequency model of the target area.

[0092] According to the method provided in this embodiment, the time-domain seismic data volume can also be converted to depth to obtain the depth-domain seismic data volume. Then, a low-frequency model can be constructed based on the depth-domain seismic data volume and well logging data. The process of constructing a low-frequency model based on the depth-domain seismic data volume and well logging data is similar to the above process, except that the data used is all in the depth domain, such as sampling depth points, depth-domain stratigraphic bedding models, depth-domain fault bedding models, depth-domain stratigraphic framework models, etc. The low-frequency model constructed based on the depth-domain seismic data volume and well logging data is also in the depth domain.

[0093] Figure 4 This application provides an apparatus for constructing a low-frequency seismic inversion model. The apparatus includes: an acquisition module 401 for acquiring seismic data volume and well logging data of a target area; and a determination module 402 for determining first geological attribute data of the target area based on the well logging data, wherein the first geological attribute data is one or more of the following: acoustic impedance data, density data, natural gamma data, sonic transit time data, permeability data, porosity data, resistivity data, conductivity data, water saturation data, potential data, and formation temperature data; determining a stratigraphic framework model of the target area based on the seismic data volume of the target area; determining a target spatial range of a target geological body of a target geological type within the target area based on the seismic data volume of the target area; determining second geological attribute data corresponding to the target spatial range; and determining a low-frequency model of the target area based on the first geological attribute data, the stratigraphic framework model, and the second geological attribute data corresponding to the target spatial range.

[0094] In one possible implementation, the target geological type is one or more of carbonate caves, river channels, and igneous bodies.

[0095] In one possible implementation, the determining module 402 is configured to: determine the seismic attribute volume of the target area based on the seismic data volume of the target area; and determine the target spatial range of the target geological body of the target geological type within the target area based on the seismic attribute volume of the target area.

[0096] In one possible implementation, the first geological attribute data of the target area includes geological attribute values ​​corresponding to multiple sampling time points in exploratory wells within the target area, wherein the exploratory wells are wells used to detect the logging data; the seismic attribute body includes seismic attribute values ​​corresponding to multiple sampling time points within the target area; and the second geological attribute data corresponding to the target spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the target spatial range. The determining module 402 is configured to: determine the target geological attribute value of the corresponding target sampling time point within the target spatial range from the first geological attribute data of the target area; and determine the second geological attribute data corresponding to the target spatial range based on the target sampling time point and the corresponding target geological attribute value; or obtain the target geological attribute value corresponding to the geological type of the target geological body, set the geological attribute values ​​corresponding to all sampling time points within the target spatial range as the target geological attribute value, and obtain the second geological attribute data corresponding to the target spatial range.

[0097] In one possible implementation, the determining module 402 is configured to: interpolate the first geological attribute data based on the stratigraphic framework model to obtain an initial low-frequency model of the target area, wherein the initial low-frequency model of the target area includes low-frequency geological attribute values ​​corresponding to multiple sampling time points within the target area; and replace the data in the initial low-frequency model corresponding to the target spatial range with the second geological attribute data corresponding to the target spatial range to obtain the low-frequency model of the target area.

[0098] In one possible implementation, the second geological attribute data corresponding to the spatial range includes geological attribute values ​​corresponding to multiple sampling time points within the spatial range; the first geological attribute data includes geological attribute values ​​corresponding to multiple sampling time points within the target area; and the stratigraphic framework model includes vector data representing multiple stratigraphic planes and fault planes within the target area. The determining module 402 is configured to: for each sampling time point among the multiple sampling time points within the target area, if the sampling time point is within the target spatial range, generate a low-frequency geological attribute value for the sampling time point based on the geological attribute value of the sampling time point in the second geological attribute data corresponding to the target spatial range and the vector data representing multiple stratigraphic planes and fault planes within the target area in the stratigraphic framework model; if the sampling time point is not within the target spatial range, generate a low-frequency geological attribute value for the sampling time point based on the second geological attribute value corresponding to the sampling time point in the first geological attribute data and the vector data representing multiple stratigraphic planes and fault planes within the target area in the stratigraphic framework model; and determine the low-frequency geological attribute value of each sampling time point within the target area as the low-frequency model of the target area.

[0099] In one possible implementation, the seismic data volume includes seismic wave amplitude values ​​corresponding to multiple sampling time points within the target area. The determining module 402 is configured to: for each sampling time point in the seismic data volume, if the seismic wave amplitude value corresponding to the sampling time point satisfies a first numerical condition among multiple pre-stored numerical conditions, then record that the sampling time point corresponds to the first numerical condition; for each numerical condition, determine the spatial range corresponding to the closed spatial region that can be formed by each sampling time point corresponding to the numerical condition, and use it as the target spatial range of the target geological body within the target area.

[0100] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. The computer device 500 can vary significantly due to differences in configuration or performance. It may include one or more central processing units (CPUs) 501 and one or more memories 502. The memories 502 store at least one instruction, which is loaded and executed by the processor 501 to implement the methods provided in the above-described method embodiments. Of course, the computer device may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The computer device may also include other components for implementing device functions, which will not be elaborated upon here.

[0101] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions that can be executed by a processor in a terminal to complete the method for constructing a low-frequency seismic inversion model in the above embodiments. The computer-readable storage medium can be non-transitory. For example, the computer-readable storage medium can be ROM (read-only memory), RAM (random access memory), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0102] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0103] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of building a low frequency model for seismic inversion, characterized in that, The method comprises: acquiring seismic data volume and logging data of a target area; determining first geological attribute data of the target area based on the logging data, the first geological attribute data being one or more of wave impedance data, density data, natural gamma data, acoustic time difference data, permeability data, porosity data, resistivity data, conductivity data, water saturation data, potential data, formation temperature data, the first geological attribute data comprising geological attribute values corresponding to multiple sampling time points within the target area; determining a stratigraphic framework model of the target area based on the seismic data volume of the target area, the stratigraphic framework model comprising vector data representing multiple stratigraphic layers and fault layers within the target area; determining a target spatial range of a target geological body of a target geological type within the target area based on the seismic data volume of the target area; determining second geological attribute data corresponding to the target spatial range, the second geological attribute data corresponding to the target spatial range comprising geological attribute values corresponding to multiple sampling time points within the target spatial range; for each of the multiple sampling time points within the target area, if the sampling time point is within the target spatial range, setting the geological attribute value corresponding to the second geological attribute data of the sampling time point as a low-frequency geological attribute value of the sampling time point, and generating a stratigraphic identification corresponding to the sampling time point according to the vector data representing multiple stratigraphic layers and fault layers within the target area in the stratigraphic framework model; if the sampling time point is not within the target spatial range, interpolating the geological attribute value corresponding to the sampling time point in the first geological attribute data under the constraint of the vector data representing multiple stratigraphic layers and fault layers within the target area in the stratigraphic framework model to generate a low-frequency geological attribute value and a stratigraphic identification of the sampling time point; and determining the low-frequency geological attribute value of each sampling time point within the target area as a low-frequency model of the target area.

2. The method of claim 1, wherein, The target geological type is one or more of carbonate cave, channel, and igneous rock body.

3. The method of claim 1, wherein, The determination of the target spatial range of the target geological body of the target geological type within the target area based on the seismic data volume of the target area comprises: determining a seismic attribute volume of the target area based on the seismic data volume of the target area; determining the target spatial range of the target geological body of the target geological type within the target area based on the seismic attribute volume of the target area.

4. The method of claim 1, wherein, The first geological attribute data of the target area comprises geological attribute values corresponding to multiple sampling time points within a pilot well of the target area, the pilot well being a well used to detect the logging data, the seismic attribute volume comprises seismic attribute values corresponding to multiple sampling time points within the target area, and the second geological attribute data corresponding to the target spatial range comprises geological attribute values corresponding to multiple sampling time points within the target spatial range; The determination of the second geological attribute data corresponding to the target spatial range comprises: In the first geologic attribute data of the target region, a target geologic attribute value corresponding to a target sampling time point in the target spatial range is determined, and second geologic attribute data corresponding to the target spatial range is determined based on the target sampling time point and the corresponding target geologic attribute value. Or A target geologic attribute value corresponding to a geologic type of the target geologic body is obtained, and geologic attribute values corresponding to all sampling time points in the target spatial range are set as the target geologic attribute value, to obtain the second geologic attribute data corresponding to the target spatial range.

5. The method of claim 1, wherein, The method further comprises: Based on the stratigraphic framework model, the first geologic attribute data is subjected to interpolation processing to obtain an initial low-frequency model of the target region, wherein the initial low-frequency model of the target region includes low-frequency geologic attribute values corresponding to multiple sampling time points in the target region. Data corresponding to the target spatial range in the initial low-frequency model is replaced by the second geologic attribute data corresponding to the target spatial range to obtain a low-frequency model of the target region.

6. The method of claim 1, wherein, The seismic data volume includes seismic wave amplitude values corresponding to multiple sampling time points in the target region, and the target spatial range information of the target geologic body in the target region is determined based on the seismic data volume of the target region, comprising: For each sampling time point in the seismic data volume, if the seismic wave amplitude value corresponding to the sampling time point satisfies a first numerical condition in the pre-stored multiple numerical conditions, the sampling time point corresponding to the first numerical condition is recorded. For each numerical condition, a closed spatial region corresponding to each sampling time point corresponding to the numerical condition is determined as a target spatial range of the target geologic body in the target region.

7. An apparatus for constructing a low frequency model for seismic inversion, characterized in that, The device comprises: An acquisition module configured to acquire a seismic data volume and logging data of a target region. The determining module is configured to determine first geological attribute data of the target region based on the logging data, the first geological attribute data being one or more of wave impedance data, density data, natural gamma data, acoustic time difference data, permeability data, porosity data, resistivity data, conductivity data, water saturation data, potential data, and formation temperature data, the first geological attribute data including geological attribute values corresponding to multiple sampling time points in the target region; determine a stratigraphic framework model of the target region based on a seismic data volume of the target region, the stratigraphic framework model including vector data representing multiple stratigraphic layers and fault layers in the target region; determine a target spatial range of a target geological body of a target geological type in the target region based on the seismic data volume of the target region; determine second geological attribute data corresponding to the target spatial range, the second geological attribute data corresponding to the spatial range including geological attribute values corresponding to multiple sampling time points in the spatial range; for each of the multiple sampling time points in the target region, if the sampling time point is within the target spatial range, set a geological attribute value corresponding to the second geological attribute data of the sampling time point as a low-frequency geological attribute value of the sampling time point, and generate a stratigraphic identification corresponding to the sampling time point according to the vector data representing the multiple stratigraphic layers and fault layers in the target region in the stratigraphic framework model; if the sampling time point is not within the target spatial range, interpolate a geological attribute value corresponding to the sampling time point in the first geological attribute data under the constraint of the vector data representing the multiple stratigraphic layers and fault layers in the target region in the stratigraphic framework model to generate a low-frequency geological attribute value and a stratigraphic identification of the sampling time point; and determine the low-frequency geological attribute value of each sampling time point in the target region as a low-frequency model of the target region.

8. A computer device, comprising: The computer device includes a processor and a memory, and the memory stores at least one instruction, which is loaded and executed by the processor to implement the operations performed by the method for constructing a low-frequency model of seismic inversion according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the operations performed by the method for constructing a low-frequency model of seismic inversion according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program product includes at least one instruction, which is loaded and executed by the processor to implement the operations performed by the method for constructing a low-frequency model of seismic inversion according to any one of claims 1 to 6.

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